Efficient aerodynamic design through evolutionary programming and support vector regression algorithms-Reference-Cited by-同舟云学术

Efficient aerodynamic design through evolutionary programming and support vector regression algorithms

Published:2012-09 Issue:12 Volume:39 Page:10700-10708
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Andrés E.,Salcedo-Sanz S.,Monge F.,Pérez-Bellido A.M.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference33 articles.

1. ACARE Advisory Council for Aeronautics Research in Europe. (2010).“Aeronautics and Air Transport: Beyond vision 2020 (towards 2050)”, .

2. Argüelles, P., Bischoff, M. et al. (2001). European Aeronautics: A vision for 2020, ACARE .

3. A grid-enabled asynchronous metamodel-assisted EA for aerodynamic optimization;Asouti;Genetic Programming and Evolvable Machines,2009

4. An overview of evolutionary algorithms for parameter optimization;Bäck;Evolutionary Computation,1993

5. Surrogate models based on function and derivative values for aerodynamic global optimization;Bompard;V European Conference on Computational Fluid Dynamics ECCOMAS CFD,2010

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